Where deep RL requires a task, meta-RL instead requires a task distribution, a large set of tasks with some form of shared structure. Meta-RL is then defined as any process that yields faster learning, on average, with each new draw from the task distribution [1]}. A classic example, leveraged in numerous meta-RL studies [2]}, [3]} is a distribution of bandit problems, each with its own sampled set of action-contingent reward probabilities.
